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Relevant bibliographies by topics / TLSNN

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Academic literature on the topic 'TLSNN'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "TLSNN"

1

Winita, Sulandari, Subanar, Suhartono, Utami Herni, Hisyam Lee Muhammad, and Canas Rodrigues Paulo. "SSA-based hybrid forecasting models and applications." Bulletin of Electrical Engineering and Informatics 9, no. 5 (2020): 2178–88. https://doi.org/10.11591/eei.v9i5.1950.

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This study attempted to combine SSA (singular spectrum analysis) with other methods to improve the performance of forecasting model for time series with a complex pattern. This work discussed two modifications of TLSAR (two-level seasonal autoregressive) modeling by considering the SSA decomposition results, namely TLSNN (two-level seasonal neural network) and TLCSNN (two-level complex seasonal neural network). TLSAR consisted of a linear trend, harmonic, and autoregressive component. In contrast, the two proposed hybrid approaches consisted of flexible trend function, harmonic, and neural net

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Sulandari, Winita, Subanar Subanar, Suhartono Suhartono, Herni Utami, Muhammad Hisyam Lee, and Paulo Canas Rodrigues. "SSA-based hybrid forecasting models and applications." Bulletin of Electrical Engineering and Informatics 9, no. 5 (2020): 2178–88. http://dx.doi.org/10.11591/eei.v9i5.1950.

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This study attempted to combine SSA (Singular Spectrum Analysis) with other methods to improve the performance of forecasting model for time series with a complex pattern. This work discussed two modifications of TLSAR (Two-Level Seasonal Autoregressive) modeling by considering the SSA decomposition results, namely TLSNN (Two-Level Seasonal Neural Network) and TLCSNN (Two-Level Complex Seasonal Neural Network). TLSAR consisted of a linear trend, harmonic, and autoregressive component. In contrast, the two proposed hybrid approaches consisted of flexible trend function, harmonic, and neural net

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Jokhio, Rizwan, M. Munir Babar, and Pir M. Ajmal. "Modeling of Flow Rate at Sukkur Barrage using Artificial Neural Networks (ANNs)." Neutron 22, no. 2 (2023): 57–64. http://dx.doi.org/10.29138/neutron.v22i2.179.

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Modeling of flow discharge plays a significant role in effective planning, sustainable usage, development, and management of water resources in short (hourly) and long-term (monthly) temporal categories. Since the inception of managing water resources, various techniques such as conceptual, metric, and physical models have been introduced all of these require a large amount of data, labor, and expense to be incorporated to obtain reliable results, due to which Artificial Intelligence methods were introduced that require less amount of data, time, expense and as well as experience to model flow

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Blase, Wolfgang, and Gerhard Cordier. "Na5[TlSn3], eine Zintl-Phase mit P4analogen Tetraederanionen." Zeitschrift für Kristallographie 196, no. 1-4 (1991): 207–11. http://dx.doi.org/10.1524/zkri.1991.196.1-4.207.

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Setiawardhana, Rudy Dikairono, Djoko Purwanto, and Tri Arief Sardjono. "Navigasi Robot Penjaga Gawang Berdasarkan Prediksi Posisi dan Waktu Kedatangan Bola." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 9, no. 3 (2020): 296–304. http://dx.doi.org/10.22146/.v9i3.295.

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Penelitian tentang robot sepakbola beroda telah banyak dikembangkan, terutama untuk proses akurasi pendeteksian dan klasifikasi objek. Makalah ini bertujuan untuk menyelesaikan permasalahan akurasi prediksi posisi bola dan waktu kedatangan di gawang serta navigasi robot untuk memblokade bola agar tidak melintasi gawang. Penelitian sebelumnya menggunakan metode Single Layer Neural Network (SLNN) dan Two Layer Neural Network (TLNN). Makalah ini membuat algoritme Modified Two-Layer Neural Network (MTLNN) untuk peningkatan akurasi prediksi posisi dengan waktu kedatangan bola dan Goalkeeper Robot N

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Huang, Daping, and John D. Corbett. "K4Au(TlSn3): A Novel Zintl Phase with an Anionic Chain." Inorganic Chemistry 37, no. 19 (1998): 5007–10. http://dx.doi.org/10.1021/ic980579q.

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HUANG, D., and J. D. CORBETT. "ChemInform Abstract: K4Au(TlSn3): A Novel Zintl Phase with an Anionic Chain." ChemInform 29, no. 49 (2010): no. http://dx.doi.org/10.1002/chin.199849003.

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Tziotzios, Georgios, Xanthoula Eirini Pantazi, Charalambos Paraskevas, et al. "Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits." Horticulturae 10, no. 3 (2024): 251. http://dx.doi.org/10.3390/horticulturae10030251.

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The current study investigates the use of a non-destructive hyperspectral imaging approach for the evaluation of kiwifruit cv. “Hayward” internal quality, focusing on physiological traits such as soluble solid concentration (SSC), dry matter (DM), firmness, and tannins, widely used as quality attributes. Regression models, including partial least squares regression (PLSR), bagged trees (BTs), and three-layered neural network (TLNN), were employed for the estimation of the above-mentioned quality attributes. Experimental procedures involving the Specim IQ hyperspectral camera utilization and so

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Brahmaiah, Veeramosu Priyanka, Yarlagadda Padma Sai, and Mahendra N. Giri Prasad. "Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network." Traitement du Signal 38, no. 4 (2021): 1161–69. http://dx.doi.org/10.18280/ts.380427.

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Epileptic seizure is one which affects the normal brain activities of human being and considered to be a risky disease. The eye ball movement signals pattern plays a significant role in determining the epileptic seizure in precise manner. In addition to it, EOG signals has its influence in detecting epileptic seizure through assessment of eye ball movement signals precisely. Detecting Epilepsy using genetical based Convolutional Neural Network plays a major role in the previous research works. Conversely, the existence of background noise on eye ball signals may impact on the outcome failure.

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Liu, Ting-Yu, Peng Zhang, Juan Wang, and Yi-Feng Ling. "Compressive Strength Prediction of PVA Fiber-Reinforced Cementitious Composites Containing Nano-SiO2 Using BP Neural Network." Materials 13, no. 3 (2020): 521. http://dx.doi.org/10.3390/ma13030521.

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In this study, a method to optimize the mixing proportion of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites and improve its compressive strength based on the Levenberg-Marquardt backpropagation (BP) neural network algorithm and genetic algorithm is proposed by adopting a three-layer neural network (TLNN) as a model and the genetic algorithm as an optimization tool. A TLNN was established to implement the complicated nonlinear relationship between the input (factors affecting the compressive strength of cementitious composite) and output (compressive strength). An orthogonal e

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Book chapters on the topic "TLSNN"

1

Nanthini, M., and K. Pradeep Mohan Kumar. "TLRNN: Two-Level RNN Based Personalized Recommendation in Tourism Domain." In Cyber Technologies and Emerging Sciences. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2538-2_19.

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Rastogi, Swati, Siddhartha P. Duttagupta, and Anirban Guha. "Digital Image Conspicuous Features Classification Using TLCNN Model with SVM Classifier." In Pattern Recognition and Image Analysis. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04881-4_39.

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Conference papers on the topic "TLSNN"

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Deepa, N., and Devi T. "E-TLCNN Classification using DenseNet on Various Features of Hypertensive Retinopathy (HR) for Predicting the Accuracy." In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2021. http://dx.doi.org/10.1109/iciccs51141.2021.9432255.

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